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• 专论：第30届计算机技术与应用学术会议 (CACIS 2019 雅安) • 上一篇    下一篇

城市交通事故的局部相关性可视分析方法

  

  1. (1. 合肥工业大学计算机与信息学院，安徽 合肥 230601； 2. 合肥市公安局交通警察支队，安徽 合肥 230009)
  • 出版日期:2019-10-31 发布日期:2019-11-06
  • 基金资助:
    安徽省科技攻关计划项目(1604d0802009)；国家重点研发计划项目(2017YFB1402200)；国家自然科学基金项目(61602146)；安徽省高等学 校省级质量工程项目(2017jyxm0045)；中央高校基本科研业务费专项(JZ2017HGBH0915)

Visual Analytics Method for Local Correlation of Urban Traffic Accidents

  1. (1. School of Computer Science and Information Technology, Hefei University of Technology, Hefei Anhui 230601, China;  2. Traffic Police Division, Public Security Bureau of Hefei Municipality, Hefei Anhui 230009, China)
  • Online:2019-10-31 Published:2019-11-06

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摘要： 交通事故数据蕴含有交通事故规律，如交通事故与天气、时间、道路等因素的关 联规律，值得深入挖掘。虽然天气、时间、道路等因素对交通事故均有影响，但对不同区域交 通事故的影响不尽相同，即具有局部相关性。挖掘局部相关性能更好地揭示这些因素与交通事 故之间的相关性。为此提出一套分析挖掘交通事故数据中所蕴含的局部相关性的方法。首先基 于交通事故数据提取事故多发路段，每个事故多发路段包含有位置、时间以及相关的交通事故 信息；然后提出一套聚类支持的局部相关性可视分析方法分析事故多发路段：①以待分析因素 直方图(如天气直方图、时间直方图)刻画事故多发路段；②基于直方图相似性对事故多发路段 进行聚类分析；③在多关联视图支持的交互环境中进一步观察、分析聚类结果以挖掘待分析因 素与交通事故之间的局部相关性。通过分析安徽省合肥市 2015—2018 年交通事故接警数据，取 得了一些有意义的分析结果，验证了该方法的有效性。

关键词: 交通事故, 可视分析, 局部相关性, 事故多发路段

Abstract: Traffic accident data may contain meaningful patterns of traffic accident, such as correlations between traffic accident and weather, time, road, etc. It is worthy of in-depth study. In general, the traffic accidents are correlated with weather, time and road. However, the correlation effect is different among various regions, which means there are local correlations between those factors and traffic accidents. It is valuable to reveal the relation between these factors and traffic accidents by analyzing local correlations. The paper presents a method to discover local correlations in traffic accidents. Firstly, the method extracts accident-prone road segments, each of which contains location, time and some other related accident information. A cluster-supported local correlation visual analysis method is presented to analyze accident-prone road segments: some histograms of these factors (weather histogram, time histogram) are used to feature accident-prone road segments, and a cluster algorithm is applied to analyze accident-prone road segments based onthe similarity of histograms. The cluster results are further interactively analyzed in linked-views to discover local correlations. The method is used to analyze traffic accident data of Hefei by specialists, and some meaningful local correlations are found, which demonstrates the method’s effectiveness.

Key words:  traffic accidents, visual analysis, local correlations, accident-prone road segments